U.S. patent application number 13/341029 was filed with the patent office on 2012-04-26 for predictive text dictionary population.
This patent application is currently assigned to Research In Motion Limited. Invention is credited to Jason Griffin, Frank M. Tyneski.
Application Number | 20120101811 13/341029 |
Document ID | / |
Family ID | 34929233 |
Filed Date | 2012-04-26 |
United States Patent
Application |
20120101811 |
Kind Code |
A1 |
Griffin; Jason ; et
al. |
April 26, 2012 |
PREDICTIVE TEXT DICTIONARY POPULATION
Abstract
A method and system for populating a predictive text dictionary
is provided. A connection between a handheld electronic device and
a network is detected. The handheld electronic device is operable
to allow a user to enter text. The handheld electronic device has a
predictive text dictionary that is operable to receive and employ
sets of words. User preferences for the handheld electronic device
are retrieved. The predictive text dictionary of the handheld
electronic device is populated with a set of words at least
partially based on the user preferences.
Inventors: |
Griffin; Jason; (Kitchener,
CA) ; Tyneski; Frank M.; (Kitchener, CA) |
Assignee: |
Research In Motion Limited
Waterloo
CA
|
Family ID: |
34929233 |
Appl. No.: |
13/341029 |
Filed: |
December 30, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11080444 |
Mar 16, 2005 |
8112708 |
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13341029 |
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10870035 |
Jun 18, 2004 |
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11080444 |
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Current U.S.
Class: |
704/10 ;
704/E11.001 |
Current CPC
Class: |
G06F 40/242
20200101 |
Class at
Publication: |
704/10 ;
704/E11.001 |
International
Class: |
G06F 17/21 20060101
G06F017/21 |
Claims
1-20. (canceled)
21. A method of populating a predictive text dictionary of a
computing device, the method comprising: sending a request to a
server for a new word, wherein the new word is selected at least
partially based on a user preference associated with the computing
device; receiving the new word from the server; and updating a
predictive text dictionary of the computing device to reflect
addition of the new word.
22. The method of claim 21, wherein updating a predictive text
dictionary further comprises: appending the new word to the
predictive text dictionary.
23. The method of claim 21, wherein updating a predictive text
dictionary further comprises: determining whether to discard an
existing word from the predictive text dictionary; and discarding
the existing word from the predictive text dictionary.
24. The method of claim 23, wherein determining whether to discard
an existing word from the predictive text dictionary further
comprises: receiving from the server a user preference associated
with the computing device; and determining whether a word category
is removed in the user preference.
25. The method of claim 24, wherein discarding the existing word
from the predictive text dictionary further comprises: discarding a
word associated with the removed word category.
26. The method of claim 23, wherein determining whether to discard
an existing word from the predictive text dictionary further
comprises: determining whether to discard the existing word based
on an expiry date associated with the existing word.
27. The method of claim 23, wherein determining whether to discard
an existing word from the predictive text dictionary further
comprises: determining whether to discard the existing word based
on usage of the word in the computing device.
28. The method of claim 23, wherein determining whether to discard
an existing word from the predictive text dictionary further
comprises: determining whether to discard the existing word based
on a location of the computing device.
29. A computer-readable medium having computer executable
instructions stored thereon for execution on a processor of a
device so as to implement the method of claim 21.
30. A computing device comprising: a memory storing a predictive
text dictionary; a communication module configured to: send a
request to a server for a new word, wherein the new word is
selected at least partially based on a user preference associated
with the computing device; and receive the new word from the
server; and a processor configured to: update the predictive text
dictionary to reflect addition of the new word.
31. The computing device of claim 30, wherein the processor is
further configured to: append the new word to the predictive text
dictionary.
32. The computing device of claim 30, wherein the processor is
further configured to: determine whether to discard an existing
word from the predictive text dictionary; and discard the existing
word from the predictive text dictionary.
33. The computing device of claim 30, wherein the communication
module is further configured to receive a user preference
associated with the computing device from the server, and the
processor is further configured to: determine whether a word
category is removed in the user preference; and discard a word
associated with the removed word category from the predictive text
dictionary.
34. The computing device of claim 30, wherein the processor is
further configure to: determine whether to discard an existing word
from the predictive text dictionary based on an expiry date
associated with the existing word; and discard the existing word
from the predictive text dictionary.
35. The computing device of claim 30, wherein the processor is
further configured to: determine whether to discard an existing
word from the predictive text dictionary based on usage of the
existing word in the computing device; and discard the existing
word from the predictive text dictionary.
36. The computing device of claim 30, wherein the processor is
further configured to: determine whether to discard an existing
word from the predictive text dictionary based on a location of the
computing device; and discard the existing word from the predictive
text dictionary.
37. A method of populating a predictive text dictionary of a
computing device, the method comprising: sending a request to a
server for a new word, the request including a location of the
computing device, wherein the new word is selected at least
partially based on the location of the computing device; receiving
the new word from the server; and updating a predictive text
dictionary of the computing device to reflect addition of the new
word.
38. The method of claim 37, wherein the computing device is a
wireless handheld electronic device associated with a wireless
gateway, and capable of a cellular communication with a cellular
tower.
39. The method of claim 37, wherein the computing device is a
wireless handheld electronic device and capable of determining the
location of the computing device based on cellular
triangulation.
40. The method of claim 37, wherein the computing device includes a
GPS receiver, and is capable of determining a latitude and a
longitude of the location of the computing device.
41. The method of claim 40, wherein updating a predictive text
dictionary further comprises: comparing the location of the
computing device with a previous location of the computing device
to determine a word associated with the previous location; and
discarding the word associated with the previous location from the
predictive text dictionary.
Description
FIELD OF THE INVENTION
[0001] The invention relates generally to computing device input
interfaces and, more specifically, relates to a method and system
for facilitating text entry.
BACKGROUND OF THE INVENTION
[0002] Text entry methods and systems for computing devices are
well-known in the art. While some computing devices, such as
personal computers, have been afforded with a full QWERTY keyboard
for alphanumeric text entry, many other computing devices, such as
handheld electronic devices, are equipped with limited keyboards,
wherein one key can represent more than one alphabetic character.
One such system, referred to in the art as multi-tap, has been in
use for a number of years for permitting users to enter text using
a conventional telephone key pad such as specified under ITU E
1.161. Multi-tap requires a user to press a key a varying number of
times, generally within a limited period of time, to input a
specific letter.
[0003] Another system, T9.RTM. developed by Tegic Communications,
Inc., uses predictive letter patterns to allow a user to ideally
press each key representing a letter only once to enter text.
Unlike multi-tap, which requires a user to indicate a desired
character by a precise number of presses of a key, or keystrokes,
T9 uses a predictive text dictionary and established letter
patterns for a language to intelligently guess which one of many
characters represented by a key a user intended to enter. The
predictive text dictionary is primarily a list of words, acronyms,
abbreviations, etc. that can be used in the composition of text.
Generally, all possible character string permutations represented
by a number of keystrokes entered by a user are compared to the
words in the predictive text dictionary and a subset of the
permutations is shown to the user to allow selection of the
intended character string. The permutations are generally sorted by
likelihood determined from the number of words matched in the
predictive text dictionary and various metrics maintained for these
words. Where the possible character string permutations do not
match any words in the predictive text dictionary, the set of
established letter patterns for a selected language can be applied
to suggest the most likely character string permutations. As can be
conceived, a user may be required to input a number of additional
keystrokes in order to enter in a desired word.
[0004] Despite the plethora of these and other interfaces available
for inputting text on such computing devices, the ease and speed of
text entry may not be satisfactory in some circumstances. The
predictive text dictionary may be populated with words unlikely to
be used by a user and may not contain other words more likely to be
used by that user. As a result, the user may, upon entry of a set
of keystrokes, be presented with a list of character strings that
correspond to words that the user does not intend to input.
Further, character strings corresponding to wards the user intends
to input may not appear in the suggestions provided by the
device.
[0005] Such predictive text dictionaries have been used on personal
computers and, more recently, on computing devices with limited
keyboards, to suggest a number of words to the user upon partial
entry of the word. On a personal computer, the word fragment
entered by a user in a software application is compared to words in
a predictive text dictionary to determine a list of words matching
the pattern of letters provided by the word fragment. The list of
words is then provided to the user as a list of options from which
the user can select. Where the word being entered by the user does
not appear in the list of words displayed, the user can elect to
continue entering in text to complete the word. In computing
devices with limited keyboards using systems where one keystroke
can represent a number of characters, such as T9, this function is
little different than the determination of the character strings to
show the user, as, in this case, whole words beginning with
characters possibly represented by the keystrokes entered are shown
to the user.
[0006] The provisioning of predictive text dictionaries on some
computing devices with limited non-volatile memory has proven to be
a challenge. As a result, manufacturers have been forced to equip
such devices with a pared-down predictive text dictionary, thus
reducing the words available. On the other hand, large, generic
predictive text dictionaries can be disadvantageous as a large
number of words, many of which may not be relevant to or used by
the user, can be suggested by the predictive text dictionary upon
entering in a number of keystrokes. This situation serves to
frustrate and slow down the users input of text, and even can
result in the insertion of the incorrect words.
[0007] Some computing devices permit a user to populate the
predictive text dictionary with words provided by the user, thus
increasing the utility of the predictive text dictionary if placed
therein. This method of expanding the predictive text dictionary,
however, is slow and typically needs to be repeated for each device
of the user.
[0008] It is, therefore, an object of the invention to provide a
novel method and system for populating a predictive text
dictionary.
SUMMARY OF THE INVENTION
[0009] By populating a predictive text dictionary of a computing
device based on user preferences, the predictive text dictionary
can be populated with words that are relevant to the interests,
needs, location, etc. of the user. In this manner, the words
suggested to the user using the predictive text dictionary may be
more likely to match a users intended input.
[0010] Further, by not placing words that bear little relevance to
the user in the dictionary, the suggestions provided to the user
from the predictive text dictionary are likely to be more relevant
to the user's intended input.
[0011] The maintenance of sets of words related to various
interests, professions, locations, etc. allows a predictive text
dictionary to be quickly populated with words relevant to the user.
Also, where a user maintains more than one such computing device,
or replaces one computing device with another, the invention
provides a simple method of populating the predictive text
dictionary of the new computing device(s) that does not require the
user to spend an undue amount of time entering the words into the
dictionary himself.
[0012] By maintaining a database of words related to various
interests, professions, locations, etc., and using the words in the
database to populate the predictive text dictionary of a users
computing device, words can be prospectively placed into the
dictionary for later use by the user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Certain embodiments of the invention will now be described,
by way of example only, with reference to the attached Figures,
wherein:
[0014] FIG. 1 shows a topological view of a number of components of
the invention;
[0015] FIG. 2 shows an exploded view of a cellphone;
[0016] FIG. 3 shows an exemplary user interface for selecting
categories and options for use in the invention;
[0017] FIG. 4 illustrates a method of updating a computing device's
predictive text dictionary; and
[0018] FIG. 5 illustrates a method of creating an update for a
computing device's predictive text dictionary.
DETAILED DESCRIPTION OF THE INVENTION
[0019] FIG. 1 is a schematic diagram of a number of components in
accordance with an embodiment of the invention. A mobile computing
device 104 is shown in communication via radio frequency to a cell
tower 108, which is, in turn, connected to the Internet 112 via
some wireless network infrastructure (not shown). A server 116 is
also connected to the Internet 112, and is additionally coupled to
a word database 120 and a user database 124.
[0020] The word database 120 stores a plurality of words that
correspond to a number of categories and/or locations, and a number
of other metrics corresponding to each word. A word as used herein
refers to a word, an acronym, an abbreviation, a phrase, etc.
employed by users to communicate. For example, the words
"football", "NFL", "Superbowl", "touchdown", "referee" and "ref"
could be associated with a "Sports" category. Additionally, some
words in the word database 120 are related to locations, such as
cities. For example, the words "Washington", "D.C.", "President",
"White House" and "Smithsonian" and "Pennsylvania Avenue" could be
associated with the city of Washington, D.C.
[0021] Words can be added to or deleted from the word database 120.
For example, where a user has selected the "Sports" category, the
rise of a new star NFL football player could cause his name to be
added to the category. In an embodiment, the player's name is
added, along with the date.
[0022] The user database 124 stores user preferences for each user
including the currently selected categories, the date the
categories were last updated, the date that the computing device
was last updated, the last detected location of the computing
device, and the date since which the computing device has been at
that location.
[0023] The server 116 executes a user manager that is operable to
retrieve user preferences from the user database 124, a query
module to retrieve words corresponding to the user preferences from
the word database 120, and a communications module to communicate
the retrieved words to the computing device 104.
[0024] FIG. 2 shows an exploded view of a mobile computing device
104 having a text input interface 24, a display 32, a processor 48,
memory 52 and at least one communications interface 56. The text
input interface 24 comprises a set of keys via which a user can
input text. The display 32 allows the user to view the inputted
text and any options provided to the user. The memory 52 comprises
non-volatile memory in which an operating system, applications and
persistent data are stored, and volatile memory to provide adequate
temporary memory registers for the applications and operating
system to use during execution. The processor 48 executes the
operating system and applications.
[0025] The communications interface 56 of the mobile computing
device 104 is comprised of a wireless radio and antenna and is
operable to communicate with the cell tower 128.
[0026] The mobile computing device 104 executes any of a number of
applications, such as a Wireless Application Protocol (WAP)
browser, an email application, a Short Message Service (SMS)
application, a notes application, etc.
[0027] In addition, the mobile computing device 104 executes a
predictive text application for receiving keystrokes from a user,
determining what character strings the keystrokes can represent,
and comparing the character strings to words in a predictive text
dictionary maintained in non-volatile memory. The predictive text
dictionary is a list of words maintained by the computing device
104. The predictive text dictionary maintains the categories and
locations (if any) to which each word is associated.
[0028] Metrics can also be stored in the predictive text dictionary
by the predictive text application. The metrics can correspond to
the number of times each word is actively selected by the user of
the computing device. Additionally or alternatively, the metrics
can correspond to the number of times each word is used by the
user, or is even present in a document, email, etc. received by the
user. A number of non-user-specific metrics can also be stored in
the predictive text dictionary, including general likelihoods used
to determine priority between two words not previously used by a
user. Further, a number of words in the predictive text dictionary
that are intended to be permanently located in the dictionary can
be marked as such using the metrics.
[0029] FIG. 3 shows an exemplary user interface 80 for allowing a
user to select which categories are relevant to the user. The user
interface 80 can be provided via a web page, a client application
or the like. In order to change preferences, the user launches the
user interface 80 and selects or deselects the categories he deems
as relevant. The "Sports", "Finance" and "Location-specific"
categories are shown as being selected.
[0030] The general updating of the predictive text dictionary on
the computing device 104 is shown in FIG. 4. In an embodiment, this
process occurs once per day and every time the device or its
communication interface 56 is turned on. If the communication
interface 56 of computing device 104 was turned off and is then
turned on, such as is done when a user of the computing device
boards and disembarks from an airplane, the device 104 registers
itself with the cell tower 108. At step 210, upon registration with
the cell tower 108, the computing device 104 makes a request to the
server 116 for any words to be received. The request is sent to the
cell tower 108, which, in turn, forwards the request to the server
116 over the Internet 112. The request received by the server 116
includes information regarding the identity of the computing device
104 and the location of the cell tower 108.
[0031] At step 220, the server 116 determines if there are words to
be transferred to the computing device 104. The user manager
executing on the server 116 retrieves the user preferences from the
user database 124 and determines what queries will need to be run
on the word database 120.
[0032] FIG. 5 shows the method of determining if there are words to
be transferred to the computing device 104. At step 221, it is
determined whether there are any new words in the word database 120
corresponding to the existing categories a user has selected. For
example, where a user has selected the "Sports" category, and the
addition of a new NFL football player's name to the word database
occurred after the computing device's last update, the player's
name could be flagged for transfer to the device at step 222.
[0033] At step 223, it is determined whether the user has selected
new categories. At step 224, the words corresponding to the new
categories are added to the list of words to be transferred to the
computing device 104. Then, at step 225, it is determined whether
the computing device is in a new location and whether the user
preferences indicate that the user selected to receive
location-specific words. If the answer to both questions is yes,
the words associated with the new location in the word database 120
are added to the list of words to be transferred to the computing
device 104 at step 226.
[0034] While the flow chart of FIG. 5 shows three distinct groups
of words being determined, those of skill in the art will
appreciate that there may be overlap between the three groups and
that the determination of the words to be transferred can typically
be performed with a single query. The single query is formulated by
the query module of the server 116 and made of the word database
120. The word database 120, in turn, returns a set of words that
correspond to at least one of the three steps 231, 233 and 235
above.
[0035] Returning to FIG. 4, the method proceeds to step 230, where
the list of words is transferred to the computing device 104 by the
server 116. In the implementation being described, the words are
transferred via a proprietary protocol. Upon receipt of the words,
the device updates the predictive text dictionary by appending the
words to the dictionary. The method of updating the computing
device is then complete.
[0036] It can be desirable to remove words from the predictive text
dictionary of the computing device 104 for a number of reasons.
Where the computing device may have limited memory, words can be
removed from the predictive text dictionary by examining the
metrics associated with each word and discarding those words with
the lowest values determined by a function of those metrics. The
predictive text dictionary may also be pared-down by the user or
system operator in recognition that over-population of the
dictionary with words that are not likely to be used can lead to
poor suggestions being provided to the user upon entering a number
of keystrokes. In the current implementation, this is performed in
two ways.
[0037] When a user selects to remove a category from his user
preferences via the user interface 80, the server 116 can transmit
this information to the computing device 104. In response, the
computing device can purge some or all of the words associated only
with the removed category. Where a word in the predictive text
dictionary associated with the category to be removed has been used
by the user according to the metrics maintained by the dictionary,
the word can be left in the dictionary.
[0038] Further, when a user has selected to receive
location-specific words, and has brought the computing device 104
to a new location, the server can direct the computing device to
purge all of the words associated with a prior location from the
predictive text dictionary. Where a word in the predictive text
dictionary associated with the previous location to be removed has
been used by the user according to the metrics maintained by the
dictionary, the word can be left in the dictionary.
[0039] While the invention has been described with specificity to
mobile devices, other types of computing devices with which the
invention can be used will occur to those of skill in the art. For
example, a personal computer can benefit from the invention, as the
predictive text dictionary of an application executing thereon
could be populated with words relevant to the interests,
profession, etc. of the user of the personal computer to provide
for enhanced word completion capabilities. Further, where the
computing device is a wireless device that occasionally connects to
a networked computer via a cable or other local networking means
such as WiFi or Bluetooth, the invention can benefit from reduced
rates for data communications via landline by connecting to the
server at least partially exclusively through the networked
computer.
[0040] While the illustrated embodiment discusses the use of user
options, these user options may be implicit or provided in some
other manner. For example, upon subscribing to a wireless access
service, a user may be asked if he would like to receive
location-specific information. The computing device he receives can
be preconfigured to always receive location-specific words upon
detection that he is in a different location.
[0041] The user can be provided the option of updating the
predictive text dictionary for a location to which the computing
device has yet to go. This can be advantageous where a user is
preparing for a business trip.
[0042] Words in the word database can be selected to correspond to
a specific category and location at the same time. As a result, the
predictive text dictionary of a computing device may only be
updated with the appropriate words when a user of the device
selected the appropriate category and is in the specific
location.
[0043] The server can be a cluster of servers in a single location
or can be two or more servers situated in a number of
locations.
[0044] Where a computing device is memory-restricted, the server
can selectively forward words to the computing device. Also, the
computing device can delete a group of words from the predictive
text dictionary to allow for newer words. Also, the words can be
forwarded to the computing device via other means of communication
apart from a proprietary protocol, such as email, MMS, WAP push and
the like.
[0045] While the location of the computing device is determined by
determining which cell tower the computing device is connected
through, other methods of locating the computing device known to
those skilled in the art can be used. For example, the computing
device can, in some circumstances, be located by determining the
node on the Internet closest to the cell tower, generally
associated with a wireless gateway for the cellular service
provider. Cellular triangulation can also be used. Further, if the
computing device is equipped with a GPS receiver, the device can
communicate the GPS-received location along with the request for
new words.
[0046] The words provided to the computing device for population of
its predictive text dictionary can be set to expire after a period
of time and/or period of non-use. This could be advantageous with
current event items. In such a case, the server can selectively
retransmit a word previously transmitted to a computing device if
it is believed the word will be used again. Additionally,
location-specific words could be set to expire upon relocation of
the computing device.
[0047] The user database can alternatively reside either on the
computing device and be transmitted to the server when requesting
updates or can reside on a computer associated with the computing
device.
[0048] Additionally, the words can be provided to the computing
device by other means. For example, the words can be placed on a
subscriber identity module ("SIM") card which are incorporated in
the predictive text dictionary upon insertion of the SIM card and
initialization of the computing device or predictive text
application thereon. Other methods will occur to those skilled in
the art.
[0049] The above-described embodiments of the invention are
intended to be examples of the invention and alterations and
modifications may be effected thereto, by those of skill in the
art, without departing from the scope of the invention which is
defined solely by the claims appended hereto.
* * * * *